Title: Why are random matrix eigenvalues cool
1Why are random matrix eigenvalues cool?
- Alan Edelman
- MIT Dept of Mathematics,
- Lab for Computer Science
- MAA Mathfest 2002
- Thursday, August 1
2Message
- Ingredient Take Any important mathematics
- Then Randomize!
- This will have many applications!
3Some fun tidbits
- The circular law
- The semi-circular law
- Infinite vs finite
- How many are real?
- Stochastic Numerical Algorithms
- Condition Numbers
- Small networks
- Riemann Zeta Function
- Matrix Jacobians
4Girkos Circular Law, n2000
Has anyone studied spacings?
5Wigners Semi-Circle
- The classical most famous rand eig theorem
- Let S random symmetric Gaussian
- MATLAB Arandn(n) S(AA)/2
- Normalized eigenvalue histogram is a semi-circle
- Precise statements require n?? etc.
6Wigners Semi-Circle
- The classical most famous rand eig theorem
- Let S random symmetric Gaussian
- MATLAB Arandn(n) S(AA)/2
- Normalized eigenvalue histogram is a semi-circle
- Precise statements require n?? etc.
n20 s30000 d.05 matrix size, samples,
sample dist e gather up
eigenvalues im1 imaginary(1) or
real(0) for i1s, arandn(n)imsqrt(-1)randn(
n)a(aa')/(2sqrt(2n(im1))) veig(a)'
ee v end hold off m xhist(e,-1.5d1.5)
bar(x,mpi/(2dns)) axis('square') axis(-1.5
1.5 -1 2) hold on t-1.011
plot(t,sqrt(1-t.2),'r')
7Elements of Wigners Proof
- Compute E(A2k)11 mean(?2k) (2k)th moment
- Verify that the semicircle is the only
distribution with these moments - (A2k)11 ?A1xAxyAwzAz1 paths of length 2k
- Need only count number of special paths of length
2k on k objects (all other terms 0 or
negligible!) - This is a Catalan Number!
8Catalan Numbers
- ways to parenthesize (n1)
objects - Matrix Power Term Graph
- (1((23)4)) A12A23A32A24A42A21
- (((12)3)4) A12A21A13A31A14A41
- (1(2(34))) A12A23A34A43A32A21
- ((12)(34)) A12A21A13A34A43A31
- ((1(23))4) A12A23A32A21A14A41
- number of special paths on n departing from 1
once - Pass 1, (loadadvance, multiplyretreat), Return
to 1
9Finite Versions
10How many eigenvalues of a random matrix are real?
gtgt eeig(randn(7)) e 1.9771
1.3442 0.6316 -1.1664
1.3504i -1.1664 - 1.3504i -2.1461 0.7288i
-2.1461 - 0.7288i
gtgt eeig(randn(7)) e -2.0767 1.1992i
-2.0767 - 1.1992i 2.9437 0.0234
0.4845i 0.0234 - 0.4845i 1.1914 0.3629i
1.1914 - 0.3629i
gtgt eeig(randn(7)) e -2.1633
-0.9264 -0. 3283 2.5242 1.6230
0.9011i 1.6230 - 0.9011i 0.5467
3 real 1 real
5 real
7x7 random Gaussian
11How many eigenvalues of a random matrix are real?
n7
0.00069 0.08460 0.57727 0.33744
7 reals 5 reals 3 reals 1 real
12How many eigenvalues of a random matrix are real?
n7
0.00069 0.08460 0.57727 0.33744
7 reals 5 reals 3 reals 1 real
These are exact but hard to compute! New research
suggests a Jack polynomial solution.
13How many eigenvalues of a random matrix are real?
- The Probability that a matrix has all real
eigenvalues is exactly - Pn,n2-n(n-1)/4
- Proof based on Schur Form
14Gram Schmidt (or QR) Stochastically
- Gram Schmidt
- Orthogonal Transformations to Upper
Triangular Form - A Q R (orthog upper triangular)
15Orthogonal Invariance of Gaussians
Qrandn(n,1) ? randn(n,1) If Q orthogonal
Q?
16Orthogonal Invariance
Qrandn(n,1) ? randn(n,1) If Q orthogonal
Q?
17Chi Distribution
norm(randn(n,1)) ? ?n
?n
18Chi Distribution
norm(randn(n,1)) ? ?n
?n
19Chi Distribution
norm(randn(n,1)) ? ?n
?n
n need not be integer
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69Same idea sym matrix to tridiagonal form
Same eigenvalue distribution as GOE O(n)
storage !! O(n) computation (potentially)
70Same idea General beta
beta 1 reals 2 complexes 4 quaternions
Bidiagonal Version corresponds To Wishart
matrices of Statistics
71Numerical Analysis Condition Numbers
- ?(A) condition number of A
- If AU?V is the svd, then ?(A) ?max/?min .
- Alternatively, ?(A) ?? max (AA)/?? min (AA)
- One number that measures digits lost in finite
precision and general matrix badness - Smallgood
- Largebad
- The condition of a random matrix???
72Von Neumann co.
- Solve Axb via x (AA) -1A b
- M ?A-1
- Matrix Residual AM-I2
- AM-I2lt 200?2 n ?
- How should we estimate ??
- Assume, as a model, that the elements of A are
independent standard normals!
?
73Von Neumann co. estimates (1947-1951)
- For a random matrix of order n the expectation
value has been shown to be about n - Goldstine, von Neumann
- we choose two different values of ?, namely n
and ?10n - Bargmann, Montgomery, vN
- With a probability 1 ? lt 10n
- Goldstine, von Neumann
-
X ?
74Random cond numbers, n??
Distribution of ?/n
Experiment with n200
75Finite n
76Small World Networks 6 degrees of separation
- Edelman, Eriksson, Strang
- Eigenvalues of ATPTP, Prandperm(n)
- Incidence
matrix of graph with two - superimposed cycles.
-
-
-
-
77Small World Networks 6 degrees of separation
- Edelman, Eriksson, Strang
- Eigenvalues of ATPTP, Prandperm(n)
- Incidence
matrix of graph with two - superimposed cycles.
-
- Wigner style derivation counts number of paths on
a tree starting and ending at the same point
(tree no accidents!) (McKay) - We first discovered the formula using the
superseeker - Catalan number answer d2n-1-? d2j-1(d-1)n-j1Cn-j
78The Riemann Zeta Function
On the real line with xgt1, for example
May be analytically extended to the complex
plane, with singularity only at x1.
79The Riemann Hypothesis
-3 -2 -1 0 ½ 1
2 3
All nontrivial roots of ?(x) satisfy
Re(x)1/2. (Trivial roots at negative even
integers.)
80The Riemann Hypothesis
Zeros .5i 14.134725142 21.022039639
25.010857580 30.424876126 32.935061588
37.586178159 40.918719012 43.327073281
48.005150881 49.773832478 52.970321478
56.446247697 59.347044003
?(x) along Re(x)1/2
-3 -2 -1 0 ½ 1
2 3
All nontrivial roots of ?(x) satisfy
Re(x)1/2. (Trivial roots at negative even
integers.)
81Computation of Zeros
- Odlyzkos fantastic computation of 10k1
through 10k10,000 for k12,21,22. - See http//www.research.att.com/amo/zeta_tables/
- Spacings behave like the eigenvalues of
- Arandn(n)irandn(n) S(AA)/2
82Nearest Neighbor Spacings Pairwise Correlation
Functions
83Painlevé Equations
84Spacings
- Take a large collection of consecutive
zeros/eigenvalues. - Normalize so that average spacing 1.
- Spacing Function Histogram of consecutive
differences (the (k1)st the kth) - Pairwise Correlation Function Histogram of all
possible differences (the kth the jth) - Conjecture These functions are the same for
random matrices and Riemann zeta
85Some fun tidbits
- The circular law
- The semi-circular law
- Infinite vs finite
- How many are real?
- Stochastic Numerical Algorithms
- Condition Numbers
- Small networks
- Riemann Zeta Function
- Matrix Jacobians
86Matrix Factorization Jacobians
General
ALU AU?VT AX?X-1
AQR AQS (polar)
? uiin-i
? riim-i
? (?i2- ?j2)
? (?i?j)
? (?i-?j)2
Sym
Orthogonal
?sin(?i ?j)sin (?i- ?j)
Tridiagonal
TQ?QT
? (ti1,i)/ ?qi
87Why cool?
- Why is numerical linear algebra cool?
- Mixture of theory and applications
- Touches many topics
- Easy to jump in to, but can spend a lifetime
studying researching - Tons of activity in many areas
- Mathematics Combinatorics, Harmonic Analysis,
Integral Equations, Probability, Number Theory - Applied Math Chaotic Systems, Statistical
Mechanics, Communications Theory, Radar Tracking,
Nuclear Physics - Applications
- BIG HUGE SUBJECT!!